Book ChapterDOI
System Identification I
Biao Huang,Yutong Qi,Akm Monjur Murshed +2 more
- pp 31-56
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The article was published on 2012-12-11. It has received 1704 citations till now. The article focuses on the topics: Nonlinear system identification & System identification.read more
Citations
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Journal ArticleDOI
Parameter estimation and reliable fault detection of electric motors
TL;DR: Based on enhanced model structures of electric motors that accommodate both normal and faulty modes, the authors introduces bias-corrected least squares (LS) estimation algorithms that incorporate functions for correcting estimation bias, forgetting factors for capturing sudden faults, and recursive structures for efficient real-time implementation.
Journal ArticleDOI
Indirect adaptive control using the novel online hypervolume-based differential evolution for the four-bar mechanism
TL;DR: An indirect adaptive control based on online multi-objective optimization is proposed to regulate the speed of the four-bar mechanism and increase its lifetime by smoothing the control action under the effects of uncertainties.
Journal ArticleDOI
Representation and identification of non-parametric nonlinear systems of short term memory and low degree of interaction
Er-Wei Bai,Roberto Tempo +1 more
TL;DR: A new representation is proposed which is particularly useful for a class of non-parametric nonlinear systems that have short term memory and low degree of interaction and is compared to existing methods both theoretically and numerically.
Proceedings ArticleDOI
Stability analysis of an adaptive Wiener structure
Robert Dallinger,Markus Rupp +1 more
TL;DR: It is shown that a simple gradient method used to adaptively fit a simplified Wiener model can be formulated as a proportionate normalised least mean squares (PNLMS) algorithm and conditions for stability in the mean square sense can be deduced.
Journal ArticleDOI
Subspace Identification of Transfer Function Models for an Unstable Bioreactor
C. Sankar Rao,M. Chidambaram +1 more
TL;DR: In this article, the identification of transfer function model of an unstable bioreactor by a subspace-based identification method is dealt with, where the data set is generated from the simulated closed loop system by giving a random signal at the reference signal.
References
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Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Journal ArticleDOI
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Journal ArticleDOI
Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
Milan Korda,Igor Mezic +1 more
TL;DR: This work extends the Koopman operator to controlled dynamical systems and applies the Extended Dynamic Mode Decomposition (EDMD) to compute a finite-dimensional approximation of the operator in such a way that this approximation has the form of a linearcontrolled dynamical system.
Journal ArticleDOI
A Tour of Reinforcement Learning: The View from Continuous Control
TL;DR: The authors surveys reinforcement learning from the perspective of optimization and control, with a focus on continuous control applications, and reviews the general formulation, terminology, and techniques for reinforcement learning for continuous control.
Journal ArticleDOI
SPICE: A Sparse Covariance-Based Estimation Method for Array Processing
Petre Stoica,Prabhu Babu,Jian Li +2 more
TL;DR: This paper presents a novel SParse Iterative Covariance-based Estimation approach, abbreviated as SPICE, to array processing, obtained by the minimization of a covariance matrix fitting criterion and is particularly useful in many- snapshot cases but can be used even in single-snapshot situations.